Houlsby, Neil

Parameter-efficient transfer learning for NLP

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 2,270

MLP-Mixer: An all-MLP Architecture for Vision

Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 2,087

Big Transfer (BiT): General Visual Representation Learning

Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2019) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,039

Big Transfer (BiT): General Visual Representation Learning

Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2020) · Lecture Notes in Computer Science

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 1,039

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Scaling Vision Transformers

Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 891

Bayesian Active Learning for Classification and Preference Learning

Houlsby, N., Huszár, F., Ghahramani, Z., & Lengyel, M. (2011) · arXiv

Machine Learning and AlgorithmsGaussian Processes and Bayesian Inference

Cited by 507

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

Self-Supervised GANs via Auxiliary Rotation Loss

Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 328

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 250

Revisiting the Calibration of Modern Neural Networks

Minderer, M., Djolonga, J., Romijnders, R., Hubis, F., Zhai, X., Houlsby, N., Tran, D., & Lucic, M. (2021) · arXiv

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 160

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

Scaling vision transformers to 22 billion parameters

Dehghani, M., Djolonga, J., Mustafa, B., Padlewski, P., Heek, J., Gilmer, J., Steiner, A., Caron, M., Geirhos, R., Alabdulmohsin, I., et al. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 112

On Robustness and Transferability of Convolutional Neural Networks

Djolonga, J., Yung, J., Tschannen, M., Romijnders, R., Beyer, L., Kolesnikov, A., Puigcerver, J., Minderer, M., D'Amour, A., Moldovan, D., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 98

On Robustness and Transferability of Convolutional Neural Networks

Djolonga, J., Yung, J., Tschannen, M., Romijnders, R., Beyer, L., Kolesnikov, A., Puigcerver, J., Minderer, M., D'Amour, A., Moldovan, D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 98

The Benchmark Lottery

Dehghani, M., Tay, Y., Gritsenko, A. A., Zhao, Z., Houlsby, N., Diaz, F., Metzler, D., & Vinyals, O. (2021) · arXiv

Machine Learning and Data ClassificationMachine Learning and Algorithms

Cited by 48

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

Dehghani, M., Mustafa, B., Djolonga, J., Heek, J., Minderer, M., Caron, M., Steiner, A., Puigcerver, J., Geirhos, R., Alabdulmohsin, I., et al. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 45

From Sparse to Soft Mixtures of Experts

Puigcerver, J., Riquelme, C., Mustafa, B., & Houlsby, N. (2023) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 36

PaliGemma: A versatile 3B VLM for transfer

Beyer, L., Steiner, A., Pinto, A. S., Kolesnikov, A., Wang, X., Salz, D., Neumann, M., Alabdulmohsin, I., Tschannen, M., Bugliarello, E., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 17